Quantization Effects on Neural Operator Conditioning
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Bibliographic record
Abstract
This work explores preliminary theoretical analysis of quantizationeffects on neural operator conditioning, where deployment in resourceconstrainedenvironments necessitates finite-precision techniques thatmay impact numerical stability [6, 5]. We propose an exploratoryframework for analyzing quantization-conditioning interactions, buildingupon established quantization literature with theoretical boundson conditioning degradation [6, 2]. Preliminary experiments throughsimulated quantization on small networks suggest INT8 quantizationincreases condition numbers by factors of 1.18±0.12, though findingsare based entirely on software simulation without hardware validation.These ideas may motivate future research in quantization-aware neuraloperator design while highlighting critical needs for hardware-basedvalidation and large-scale empirical studies before practical deploymentrecommendations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it